Classification of Heart Sounds Using Convolutional Neural Network

Applied Sciences, vol. 10, pp. 3956

Abstract

Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global average pooling layer to obtain global information about the feature maps and avoid overfitting. Considering the class imbalance, the class weights were set in the loss function during the training process to improve the classification algorithm’s performance. Stratified five-fold cross-validation was used to evaluate the performance of the proposed method. The mean accuracy, sensitivity, specificity and Matthews correlation coefficient observed on the PhysioNet/CinC Challenge 2016 dataset were 86.8%, 87%, 86.6% and 72.1% respectively. The proposed algorithm’s performance achieves an appropriate trade-off between sensitivity and specificity.

Authors 5

1. Fan Li

   Dalian University of Technology

   Affiliation as printed

   School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No. 2 Linggong Street, Dalian 116024, China

2. Hong Xiang Tang

   Dalian University of Technology

   Affiliation as printed

   School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No. 2 Linggong Street, Dalian 116024, China

3. Shang Shang

   Dalian University of Technology

   Affiliation as printed

   School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No. 2 Linggong Street, Dalian 116024, China

4. Klaus Mathiak Aachen Department of Psychiatry, Psychotherapy and Psychosomatics

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany

5. Fengyu Cong corresponding

   Dalian University of Technology · University of Jyväskylä

   Affiliation as printed

   Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland

   Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province, Dalian University of Technology, No. 2 Linggong Street, Dalian 116024, China

   School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No. 2 Linggong Street, Dalian 116024, China

   School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No. 2 Linggong Street, Dalian 116024, China